Predictors and prognosis of tracheostomy in relapsing polychondritis.

OBJECTIVE: Airway obstruction can occur in patients with relapsing polychondritis (RP) with laryngeal involvement, occasionally requiring tracheostomy to avoid serious complications. Herein, we assessed the risk factors for tracheostomy and developed a risk prediction model. METHODS: Clinical characteristics of patients with RP, with and without tracheostomy, were compared using multivariate logistic regression analysis to identify risk factors. A nomogram was developed to predict the population at risk of requiring tracheostomy. RESULTS: In total, 232 patients with RP were reviewed, of whom 146 had laryngeal involvement. Among them, 21 underwent a tracheostomy. Multivariate logistic analysis identified ages ≤ 25 or ≥ 65 (p< 0.001, OR: 24.584, 95% CI: 5.310-113.815), laryngotracheal oedema (p< 0.001, OR: 26.685, 95% CI: 4.208-169.228), and pulmonary infection (p= 0.001, OR: 18.834, 95% CI: 3.172-111.936) as independent risk factors for tracheostomy. A nomogram with a C-index of 0.936 (95% CI: 0.894-0.977) was established based on the multivariate analysis. Internal bootstrap resampling (1000 repetitions) confirmed sufficient discriminatory power with a C-index of 0.926. Decision curve analysis indicated a superior net benefit of the nomogram. Tracheostomy was associated with a significant increase in the in-hospital mortality rate (p= 0.021), but it did not affect the long-term survival rate (p= 0.706). CONCLUSION: Tracheostomy is associated with an increase in the short-term mortality rate but does not affect the long-term survival rate. The nomogram developed in this study may help identify patients at high risk for tracheostomy and aid in clinical decision-making.

Relapsing polychondritis: focus on cardiac involvement.

Background: Relapsing polychondritis (RP) with cardiac involvement may present with acute cardiovascular events, and may be associated with a negative prognosis. Herein, we analyzed the clinical characteristics of RP patients with cardiac involvement. Method: RP patients, hospitalized from December 2005 to December 2021 at Peking Union Medical College Hospital (PUMCH), were screened. Univariate and multivariate logistic regression analyses were used to statistically analyze the clinical characteristics of these patients. Results: The incidence of cardiac involvement in inpatients with RP was 24.1%. Univariate logistic regression analysis revealed age, central nervous system (CNS) involvement, neutrophil-to-lymphocyte ratio (NLR) > 6.41, and disease duration > 4 years as risk factors for cardiac involvement in RP. Conversely, the incidence of tracheobronchial and chest wall involvement was significantly lower in the group with cardiac involvement. Multivariate logistic regression confirmed that age, CNS involvement, NLR > 6.41, and disease duration > 4 years were independent factors for cardiac involvement. Subsequently, we identified five well-defined clinical patterns of RP, based on the involvement of different organs in our patients, and found that the heart-brain model was significantly mutually exclusive with the airway model. Conclusion: Occurrence of cardiac involvement in RP is associated with age, CNS involvement, NLR, and disease duration. It is mutually exclusive with airway-related involvement. Regular echocardiography and electrocardiography are necessary for patients with RP.

Clinical characteristics, outcome and prognostic factors in critically ill patients with lupus nephritis.

OBJECTIVES: To describe the clinical characteristics and outcome of patients with lupus nephritis (LN) in intensive care unit (ICU), identify prognostic factors and construct a predictive model of in-ICU survival. METHODS: A total of 505 ICU admissions of lupus patients were screened and LN patients confirmed by renal biopsy were enrolled. Clinical characteristics and outcome of patients in ICU were collected. A logistic regression analysis was performed to identify independent prognostic factors and a nomogram was plotted to construct a predictive model. RESULTS: A total of 70 patients with LN were enrolled. The median age of the patients was 28.5 years, and the median course of LN was two months. Renal pathology classes indicated that 38 patients were class IV, 11 were class IV+V, and 10 were class III. The most common primary cause of ICU admission was infection in 40 patients, followed by LN in 11 patients. Forty-one patients died in ICU. The multivariate analyses revealed that lactic acid (OR 1.682 [2.130-17.944], p=0.001), gamma-glutamyl transpeptidase (OR 1.057 [1.009-1.107], p=0.020), APACHE II (OR 3.852 [1.176-12.618], p=0.026), vasopressor (OR 10.571 [1.615-69.199], p=0.014) and platelet count (OR 0.967 [0.941-0.993], p=0.013) were independently associated with ICU survival of critical LN patients. A predictive model was constructed and validated. CONCLUSIONS: This study is the first to elucidate the features and identify prognostic factors in critically ill patients with LN. These findings could help clinicians to early identify high-risk patients of mortality, which consequently may reduce the mortality of critically ill patients with LN.

ALDH3A1 driving tumor metastasis is mediated by p53/BAG1 in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is a lethal malignancy with metastasis, a major tumor feature that predominantly correlated with progression, but the molecules that mediated tumor metastasis remain elusive. To declare the critical regulatory genes, RNA sequencing data in LUAD patients was acquired from The Cancer Genome Atlas (TCGA) and found that ALDH3A1 was distinctly highly expressed in LUAD patients with metastasis (M1) compared with those without metastasis (M0), linked to the property of cancer stem cell and epithelial-mesenchymal transition (EMT). Besides, high ALDH3A1 expression predicted a poor prognosis. Knockdown of ALDH3A1 showed decreased proliferation, migration, and invasion in A549 cell line. Furthermore, BAG1 was regulated by ALDH3A1 through p53, enhanced cell proliferation, and predicted clinical prognosis. Our findings collectively uncovered a novel mechanism that orchestrates tumor cells' metastasis, and decreasing ALDH3A1 represented a potential therapeutic target for reprogramming metastasis.

Identification of prognostic factors and construction of a nomogram for patients with relapse/refractory adult-onset Still's disease.

OBJECTIVES: This study aimed to identify the risk factors for relapse/refractory adult-onset Still's disease (AOSD) and to construct and validate a prognostic nomogram for predicting the individual risk of relapse/refractory disease. METHOD: A total of 174 patients were included in our study. Univariate and multivariate logistic regression analyses were used to identify relapse/refractory-associated factors, which were used to construct nomograms. Receiver operating characteristic (ROC) curve analysis, calibration curves, and decision curve analysis (DCA) were used to assess the predictive ability of the nomograms. RESULTS: Univariate and multivariate logistic analyses showed that age, fever, disease duration, platelet count, serum ferritin level, and erythrocyte sedimentation rate were independent unfavourable factors for relapse/refractory AOSD (p < 0.05). We constructed a 6-factor nomogram based on univariate and multivariate logistic analyses. ROC analysis indicated that the area under the curve of the 6-factor nomogram in the training set and test set was 0.765 and 0.714, respectively. In addition, the calibration curves showed excellent prediction accuracy, and DCA showed superior net benefit in the 6-factor nomograms. Moreover, we evaluated the predictive effectiveness of our nomogram in females and young adults. The results showed that our 6-factor nomogram has the same predictive ability in both subgroups. CONCLUSIONS: Novel nomograms based on clinical characteristics were developed and may be applied to help predict the individual risk of poor prognosis of patients. Key Points • Logistic regression was used to identify risk factors for relapse/refractory adult-onset Still's disease. • We then constructed a nomogram for predicting disease risk. • ROC analysis, calibration curves, and DCA all showed that the nomogram exerted good prediction ability in both the training set and test set. • The nomogram has the same predictive ability in both female and young adult subgroups.

Dermatomyositis: immunological landscape, biomarkers, and potential candidate drugs.

INTRODUCTION: Dermatomyositis (DM) is a rare inflammatory disease characterized by the invasion of the skin and muscles. Environmental, genetic, and immunological factors contribute to disease pathology. To date, no bioinformatics studies have been conducted on the potential pathogenic genes and immune cell infiltration in DM. Therefore, we aimed to identify differentially expressed genes (DEGs) and immune cells, as well as potential pathogenic genes and immune characteristics, which may be useful for the diagnosis and treatment of DM. METHOD: GSE1551, GSE5370, GSE39454, and GSE48280 from Gene Expression Omnibus were included in our study. Limma, ClusterProfiler, and Kyoto Encyclopedia of Genes and Genomes were used to identify DEGs, Gene Ontology (GO), and perform pathway analyses, respectively. Cytoscape was used to construct the protein-protein interaction (PPI) network. Small-molecule drugs were identified using a connectivity map (CMap), and the TIMER database was used to identify infiltrating cells. RESULTS: DEG analysis identified 12 downregulated and 163 upregulated genes. GO analysis showed that DEGs were enriched in immune-related pathways. Ten hub genes were identified from the PPI network. Additionally, CMap analysis showed that caffeic acid, sulfaphenazole, molindone, tiabendazole, and bacitracin were potential small-molecule drugs with therapeutic significance. We identified eight immune cells with differential infiltration in patients with DM and controls. Finally, we constructed a powerful diagnostic model based on memory B cells, M1, and M2 macrophages. CONCLUSIONS: This study explored the potential molecular mechanism and immunological landscape of DM and may guide future research and treatment of DM. KEY POINTS: • We explored the molecular mechanism and immunological landscape of dermatomyositis. • GO analysis showed that DEGs were enriched in immune-related pathways. • We predicted small-molecular drugs with potential therapeutic significance based on bioanalytical techniques. • We identified six immune cells with differential infiltration in patients with DM and controls.